This will enable readers to calculate other measures that the authors of a particular paper did not choose to include (e.g., Matthews Correlation), and will provide the flexibility needed to accommodate new developments in evaluation as debates over how best to measure performance are resolved.

it will make it possible for future readers to go back to any publications that report these four values and retrofit new measures to old data.


As it is shown here, each morph is associated with Kanjis that may be used as descriptors more convenient to index a document than the term itself.

we demonstrate here how such correspondences between morphs and Kanjis can be exploited, in different ways, to improve the results of an IR system.


The difficulty-driven strategy obtains good results.

it outperforms the random strategy after a few iterations.


Not all types of shoes, however, have shoelaces.

the synset loafer is a hyponym of shoe, but loafers don't have shoelaces.


In addition, action vignettes, as defined in the user interface, will require some amount of post-processing in order to be used.

we need to handle unknown words and word sense ambiguity issues for those words that were typed in by the annotator.


Argument composition interacts with both free constituent order and verb placement.

the latter means that some members of the composed valence list must be hypothesised before the initial verb has been encountered, leading to partially underspecified valence lists (e.g.


The median of edits per revision is 1, the standard deviation is 14.5 with a minimum of 1 and a maximum of 55 edits per revision.

most of the changes in our corpus modify articles in only one particular place.


Thus, our main insight in modelling semantic forms and their interaction patterns in NL is grounded on the idea: the subsurface form space demonstrates the query intent (expresses semantics) better than superficial (lower) query syntactical features, which might vary depending on diverse query construction.

the higher is the level of abstraction for labelling, the more robust the extraction should become.


The vocabulary containing words  forms is updated as more training examples are seen and used by the feature builder.

given enough training samples of the sentence Q, the variable W and constraint R, we can deduce the pattern , which identifies how R constrains W. This pattern contains information about the ordering in Q with respect to W (recall W could be vector-valued) such that they are mapped to R. to say, every form has some specific interaction pattern with other forms when they exist together/adjacent in Q.


We summarize topics by sub-topics along time line to fully capture rapid topic evolution in Twitter.

we rank and select salient and diversified tweets as a summary of each sub-topic.


The tremendous volume of tweets suggests summarization as the key to facilitating the requirements of topic exploration, navigation, and search from hundreds of thousands of tweets.

a summary that provides representative information of topics with no redundancy and well-written sentences would be preferred.


authority) of users and content quality of tweets are taken into consideration simultaneously in a mutually reinforcing manner.

we leverage the follower-followee relationship connecting different authors, upon which the social influence of users can be inferred.


We take a time-line approach to summarize T to fully capture the rapid topic evolution over time.

the summary for the topic consists of: where K is the number of sub-topics; • at most M salient tweets as the summary for each sub-topic There are three major steps generating a time-line summary for a topic.


The former measures how easy a tweet is to read.

tweets have high readability if they are written in regular language style using ordinary vocabulary, and have low readability if not.


Rather, we want predictors that are practical and useful for analysis.

we list the following properties we want predictors to have.


One of the notable problems with the Rasch model is that it does not take into account the out-of-sample setting.

it cannot predict words that do not appear in the training set.


To tackle this problem, we used L-BFGS (Liu and Nocedal, 1989), which requires only O(K) memory, for this optimization instead.

we used the library liblbfgs (Okazaki, 2007).


Var(v) increases when some low-ability learners know the words and some high-ability learners do not.

it increases when low-ability learners know the word for some reason other than the easiness of the word, and vice versa.


we add two models/features into the log-linear framework.


Based on a phrase-structure parsing, SRL is usually formulated as a constituent classification problem.

sRL is divided into three sub-tasks: 1) pruning with a heuristic rule, 2) argument identification (AI) to recognize arguments, and 3) semantic role classification (sRC) to predict semantic types.


span with an argument in the manual annotation, the system cannot possibly get a correct prediction.

the best the system can do is to correctly label all arguments that have a counterpart node in the parse tree.


A Support Vector Regression (SVR) model aims to learn a function f : n → , which will be used to predict the value of a variable y ∈ given a feature vector X ∈ n .

given l training instances (X 1 , y 1 ), .


As our primary goal is to compare the training methods, and not to achieve the highest possible score for each data set, we use the exact same feature representations and training parameters across all experiments.

we train an averaged perceptron model for 15 iterations.


When no solutions other than the trivial one coexist, which is experimentally confirmed in our system shown later, this signals the onset of a phase transition to a non-trivial solution.

when J i j ≥ 0 holds for all spin pairs, which is the case for models of ferromagnetism, every element of the critical eigenvector x 1 has an identical sign.


This quantity IPR takes a value between 0 and 1.

as the dimensionality tends to infinity, it remains positive for localized vectors but vanishes for spread ones, so this quantity is widely used as a standard measure for characterizing the localization property of high dimensional vectors.


We adopt a supervised learning approach to our problem.

we assume that we have a set of N training examples and y i is a binary label indicating whether c i is a thoughtful comment with respect to d i .


As mentioned previously our methodology is based on existing work on this topic.


used a lexical feature based on unigram language models, which provide a principled way to statistically model text.

it is assumed that there is a reference corpus that represents high quality text, e.g.


We hypothesize that the topical relevance between a comment and its target relies more on the overlap of nouns in the two pieces of text.

hence, we consider another KL-divergence measure using only nouns in c and d. we use the unigram language models that are defined over nouns only.


The annotators were asked to judge (1) whether a comment was relevant to each segment of its corresponding speech, and if so, (2) whether the comment was a thoughtful one.

we treat each segment of a speech as a target document.


For instance, if we define the error as function of the parameter vector: then our learning task is to findθ that minimizes the squared error: where m represents the number of training examples in our training set.

we want to find the weights for each of the input features X such that their weighted sum minimizes the error across the training set.


values and rank (order) of our predicted variables are correlated to the values and ranks of the actual values.

how monotonic is the relationship between the predicted and real values.


This feature has a negative weight for translation quality which indicates that the more lexical entropy we have in our model, the worse translations we will have.

given a specific source phrase f , there should be little ambiguity of which is the best translation for it.


Although 1-local words might be useful in geolocation, the impact of infrequent terms on overall accuracy is limited.

frequent words with some geographical association might be more informative for geolocation than words with highly-local distribution but lower frequency.


KL divergence The previous best published results over NA were achieved using KL divergence and the KD-tree grid.

kL divergence is measured between the distribution of terms in a user's aggregated tweets and that in each grid cell, with the predicted location being the centroid of the most-similar grid cell.


Although it is tempting to apply these kernels to measure the similarity between the dependency (sub)trees surrounding the target words, it should be noted that our goal is to measure the similarity of particular word pairs in two sentences, and not of the sentences or their entire dependency trees.

tree kernels do not give any special treatment of the target words, and thus they even count subtrees that do not involve a target word at all.


We construct a local context vector, for each target instance according to a standard procedure for WSD (Mihalcea, 2004;Navigli, 2009).

we treat a contextual paragraph simply as a bag-of-words.


We therefore do not ignore or adhere to unit constraints, but relax them using Lagrangian relaxation.

the proposed method basically follows the constraints and treats each bunsetsu as a unit.


However, now there is no constraint under which .

although we have to consider the penalty, we can set a different value for each decision variable.


We can use this information employing the following constraint.

we introduce a constraint between the last words of the bunsetsu.


We then computed Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores (Lin, 2004) for each of the methods.

we measured the n-gram overlap between the outputs of each method and those of human subjects.


This is because PROP ignored dependency constraints and almost greedily selected bunsetsu that were composed of many important words.

pROp had less opportunity to drop unimportant words.


His syntactico-semantic modeling comes close to Nicol's (2002) implementation who argues that verb particles are spelled out instances of functional heads in the verbal domain.

haselbach (2011) argues that (i) nach either represents the head of a functional projection wP which is above VP and projects a dative argument in its specifier, cf.


The quality of a search engine is not only determined by its document ranking function, but also by the way the search results presented to the user (Turpin et al., 2009).

good snippets can help the user select relevant documents to click on.


It should also provide a good indication on how the document could be relevant, or how two documents compare with respect to relevance.

the comparison between the perceived relevance of two documents should be consistent with their intrinsic relevance.


Formally, we need to find a snippet generator g * that can maximize the expected ratio of the snippet pairs with consistent perceived relevance: (1) In this paper, a set of judged queries will be used to train g * .

for the training queries, we have document relevance judgments will be approximated by a similarity score between the query and the snippet.


Similarly, we can use gradient descent method to solve the problem of optimizing L( f ) in this pairwise setting.

the negative gradient of one pair of items in the training data can be calculated as: for one sample of pair of items, we add two samples in the gradient training data is inconsistent with ( y i1 , y i2 ).


In each iteration, there are two cases: • Case 1: If the perceived relevance of the snippet of document d 1 is larger than that of d 2 , the sentence ranking function should not be modified; • Case 2: If the perceived relevance of the snippet about document d 2 is larger, then we should modify the sentence ranking function so that the snippet of d 1 becomes more relevant and that of d 2 less relevant; In Case 2, the modification of sentence ranking function f is achieved by adding new sentence preference pairs into the training set.

for document d 1 , we should rank higher the sentences that are more relevant than the snippet of d 2 : In our implementation, d ′ is a subset of d 1 composed by the sentences that contain at least one non-stop query term.


For our snippet generation method, we approximate the perceived relevance of the snippet by the query-sentence similarity.

we use cosine similarity between the TFIDF vectors of sentences and queries.


Snippet  For the snippets generated by the search engine, its recall value is the highest of the methods tested, but its precision score is the lowest.

the snippet generator of search engines tends to select sentences with higher perceived relevance, and this way attract the users to click on the snippets.


In other words, the snippet generator of search engines tends to select sentences with higher perceived relevance, and this way attract the users to click on the snippets.

30.8% of the documents within the test collection are relevant, but 44.8% of the snippets from the search engine are assessed as relevant, compared to 33.0% and 34.5% from SUMM and our method, respectively.


It shows that these four categories of features are all useful for learning our snippet generator.

the features that reflect the relevance between queries and documents (RQD) should be taken into account in generating snippets.


Dakka and Cucerzan (2008) trained an SVM classifier by using features related to the structure of Wikipedia articles.

they introduced bag-of-words features of abstracts (Wikipedia provides short abstracts for articles), tables (infoboxes and contents boxes), and links to other articles.


Spoken sentences in this experiment did not include dialect-specific proper nouns.

the problem was how to collect sentences containing such proper nouns.


Since there is no benchmark dataset available for the task of personalized social summarization, we collected data from Delicious, one of the most popular social tagging websites.

we extracted a set of web documents, bookmark tags, and the users who bookmarked these documents to serve as the experimental dataset.


Similarly, the credibility rank of a user Rank(u i ) is defined as the increase in the posterior probability that a user is credible, normalized by prior probability P(u t i ).

we can get: (2) In our previous work, we showed that the following relations hold true regarding the credibility rank of a claim Rank(c j ) and a user Rank(u i ): where Users j is the set of users makes claim c j , and C laims i is the set of claims the user u i makes.


In this paper, we propose to incorporate the lexical information in the unsupervised CCG induction in order to capture more complex language aspects.

an additional boundary model, which defines probability distributions over the boundary part-of-speech tags, is introduced during the probability calculation for parse trees.


In CCG, words and nonterminals are associated with rich syntactic categories which capture the basic word order and subcategorization.

the categories in CCG are defined recursively: (1) There are some atomic categories, e.g.


Categories in the form of X|X are modifier categories.

modifier categories take one category as input and output the same category as result.


Since the system inputs are the golden POS tags in the treebank, we use the boundary words and the boundary POS tags interchangeably in this paper.

in some parse tree T , we consider the boundary POS tags of each constituent and define the new probabilistic model as where distribution P C C G is the basic CCG model defined in section 4.1, P BDR is the proposed boundary model, σ 〈i, j〉 means the boundary words of the constituent covered by span 〈i, j〉, and B is a special nonterminal representing the constituent spans.


In this paper, we have proposed to incorporate lexical information in the unsupervised CCG induction.

an additional boundary model is defined to capture complex language aspects, in which boundary words are generated from a special symbol independently for each span covered by tree nodes.


Figure 2 gives the algorithm for a transition-based dependency parser, where o denotes an oracle for selecting t =[o(c)] to transit to the next state in a deterministic way.

the arc-eager parser, which is a transition-based parser, selects either Left, Right, Reduce, or Shift to analyze sentences, as shown in Fig.


We proposed using transformation rules to represent graph sequences compactly under the assumption that "change is gradual" (Inokuchi and Washio, 2008).

only a small part of the structure changes, while the other part remains unchanged between successive graphs g ( j) and g ( j+1) in a graph sequence.


The figure shows that attachment scores are improved by adding the new feature templates to the conventional feature templates.

the degree of improvement is large when the beam width is small.


Thus, the cost of H is the sum of the costs of p ∈ P, such that p is included in the hypothesis (h p = 1) and is not explained (r p = 0).

the backchaining bottoms out in p. The space of candidate hypotheses is restricted by several ILP constraints.


Some of these new features are needed to support the more flexible and generic MM facet classification scheme.

we extract novel features that capture the relationship between the citing paper and the cited paper.


We go on to compare different feature sets used for citation classification.

we compare different lexical, syntactic, and positional features.


Our corpus, like corpora from some previous studies (Athar, 2011; Dong and Schäfer, 2011) [CEPF] , is taken from NLP literature.

we have taken the 2004 ACL proceedings from the ACL Anthology Reference Corpus (ARC) (Bird et al., 2008) [OEPF] .


Our new DG-derived features improve the Fanse performance across all dependency lengths compared to the C&C baseline and Reranker '10.

we have improvements of 2.86% and 1.67% over the C&C baseline and Reranker '10 respectively when the dependency length is between 21 and 25, though the number of these dependencies is relatively small.


Table 7 shows that the Fanse features with the Maltparser improve F-score relative to the C&C baseline and Reranker '10 systems.

our final result of 87.93% F-score is a 0.5% improvement over the baseline parser, and a 0.35% gain over Reranker '10.


We will use it to formalize the crosslingual representation induction task in Section 4.

let us consider the following simple interaction matrix with the corresponding inverse: when a mistake is made at time t, the rate of update is 2/(K + 1) for task i t and half as large for the other K − 1 tasks.


Let us consider the following simple interaction matrix with the corresponding inverse: That is, when a mistake is made at time t, the rate of update is 2/(K + 1) for task i t and half as large for the other K − 1 tasks.

a defines all tasks as "equally related" to any other task.


Learning maximizes the data likelihood objective with respect to model parameters θ = (W, c): The training procedure uses stochastic gradient descent: it iteratively updates parameters using a gradient at each training subsequence w t−n+1:t .

for the word representations c, the updates have the form: where η is the learning rate and L (t) (θ ) = logP θ (w t |w t−n+1:t−1 ) is the contribution of the example to the data likelihood objective.


This paper focuses on improving event pronoun resolution using both local and global semantic information.

a predicate-argument structure is proposed to represent the local semantic information about an event while the global semantic information is represented by the entity coreference chains related with various arguments in the predicate-argument structure to complement its locality.


Here, an event pronoun is a pronoun whose antecedent refers to an event.

we focus on improving event pronoun resolution using both local and global semantic information.


In principle, the features in Table 1 can be grouped into three categories: 1) Positional features: the intuition is that the antecedent of an event pronoun should be close to each other.

different kinds of distances between the anaphor and the antecedent candidate are explored, i.e.


Figure1 shows the algorithm for computing the semantic compatibility between an event pronoun and an antecedent candidate.

only those core arguments as defined in the Propbank (Palmer et al.


This paper studies the impact of both local and global semantic information for event pronoun resolution.

a predicate-argument structure is proposed to represent the local semantic information related with an event while the global semantic information via entity coreference chains is further incorporated to complement the locality of the predicate-argument structure.


a 'genitive endocentric compounds with head to the right'.

the classification is not guided by the deeper semantics, but by the paraphrase of a given compound, or by what the language expresses.


The introduction of c is key in enabling the model to have a set of constantly evolving topics.

it dampens the rich-gets-richer dynamic of the Chinese Restaurant Process in LDA.


To this end, we take a document stream from Twitter and replace the message content of tweets nominally relating to a particular event (based on hashtag analysis) with distinct tweet-length mentions of events from the Topic Detection and Tracking corpus (TDT3) 3 dataset.

our datasets contain "background events" in the original Twitter document stream that we don't have annotations for, and "novel events" from TDT that we do have annotations for, and that form the basis of our evaluation.


The parsing task is to find the most probable semantic graph G * such that where the sum is taken over all dependency derivations D for a sentence S. Similar to the model given by Ge and Mooney (2009), we assume that the dependency parse we obtain from a third party parser (here C&C) is correct.

we assume that the probability distribution over dependency structure derivations given a sentence S densely locates at the derivation D S yielded by the used syntactic dependency parser, i.e.


Various modeling approaches have been explored for this problem.

a character-level MT method (Pennell and Liu, 2011) and sequence labeling approach (Pennell and Liu, 2010) were used recently and showed good performance.


Experiments on several data sets show our methods perform competitively.

on a widely used public test set containing 303 non-standard tokens (Choudhury et al., 2007), our system yields higher 1-best accuracy (74.6%) and better coverage (94.6%) than previous work.


we strengthen modeling the sentence level in the h-uHDP model to adapt to the sentence extraction based framework.


We can see that HDP can readily be extended to as many levels as are deemed useful.

we can obtain a hierarchy of DPs, where the draw from the DP at a given node serves as a base measure for its children (Teh, 2006).


The task of update summarization aims to produce an update summary for the documents in the update epoch, assuming that users already read earlier documents in the history epoch.

we need to boost sentences in update epoch that can bring out important and novel information.


To avoid aspect redundancy in the summary, we also adopt the MMR strategy in the process of sentence selection.

for each s m , we compute the semantic similarity between s m and each sentence s t in set Y as follows: 6 Experiments In our experiments, we use four years of TAC (2008)(2009)(2010)(2011) data, which contain 44-48 topics per year.


17, we can see that is used to balance the prize for the divergence from history epoch and penalty on the divergence from the update epoch.

when is set as the value of 0, the scoring of sentences is only determined by docset H. a sentence is likely to be selected into summary, only when it has a large divergence of aspect distribution from docset H. when is set a larger value, penalty on the divergence from docset U is more considered.


 Trigger type/ Event type : the type of an event;  Argument: the entity mentions involved in an event;  Argument role : the relation of an argument to an event where it part icipates.

the event extraction task is div ided into four co mponents:  Trigger identification: to distinguish true trigger mentions from pseudo trigger mentions;  Event type determinati on: to classify trigger mentions by event types;  Argument i dentification: to distinguish true arguments from pseudo arguments;  Argument role determinati on: to classify arguments by argument roles.


In the literature, most of existing studies on event extraction concern English and can be classified into either pattern-based (e.g., Riloff, 1996;Yangarber et al., 2000;Stevenson and Greenwood, 2005;Shinyama and Sekine, 2006;Patwardhan and Riloff, 2007;Chambers and Jurafsky, 2011) or classifier-based (e.g., Grishman et al., 2005;Ahn, 2006;Hardy et al., 2006;Maslennikov and Chua, 2007;Ji and Grishman, 2008;Patwardhan and Riloff, 2009;Liao and Grishman, 2010;Hong et al., 2011;Lu and Roth, 2012;Llorens et al., 2012).

while earlier studies focus on sentence-level extract ion, later ones turn to employ global info rmation.


In this paper, the governing sememe is introduced to recognize HMs fro m those triggers with the Coordinative structure.

if a morpheme represents the governing sememe, it is recognized as HM of that trigger.


To examine the low quality of annotated corpora in Chinese event extraction, we take the ACE (Automatic Content Extraction) 2005 Chinese corpus (with 8 types and 33 subtypes of events), one of the most popular corpora in event extraction, as an example.

we randomly select 33 documents from the training set and ask two human annotators to manually tag event mentions and their types following the definition of the ACE 2005 corpus.


Here, notation "+" signifies that the MLN contains an instance of the formula, with a separate weight, which is learnt from the training set.

the open-source Alchemy package 4 is employed for learning and inference.


Similar to previous studies, we treat these subtypes simply as 33 separate event types and do not consider the hierarchical structure among them.

we randomly select 567 documents as the training set and the remaining 66 documents as the test set.


Looking deeper into their joint models, we find that on average, the score corresponding to the POS features only is 1/50 of the score of the syntactic features in a returned joint result.

the POS features have little impact on determining the best joint result.


On the other hand, the improved tagging accuracy can further help parsing.


We can see that we achieve best results on all three cases.

a few interesting conclusions can be drawn.


Whether a tweet is selected as a representative depends on two factors: its salience score and its similarity to the already selected tweets.

a tweet is chosen if it is the candidate with the greatest salience score and its similarity to any selected tweet is below a threshold ε 8 .


This work investigates easy-first method on Chinese POS tagging, dependency parsing and joint tagging and dependency parsing.

we generalize the easy-first dependency parsing algorithm to a general framework and apply this framework to Chinese POS tagging and dependency parsing.


For joint tagging and dependency parsing, we achieve significant improvement on both the two sub-tasks.

we achieve the tagging accuracy of 94.27 which is the highest score reported on the same data set so far.


Then labelling dep performs the given action.

labelling dep (p i , attach_Left) set p i as the child of its left neighbour in the pending list.


Head-modifier relationships can be directly read-off the dependency tree.


Before we go into the details about the algorithm, one thing should be mentioned.

for dependency parsing, POS tag serves as indispensable information (Li et al., 2010).


The labelling function of the joint task, labelling Joint , behaves differently on the two types of labels.

labelling Joint (p i , ) calls labelling POS (w i , x) which associates w i , the root of p i , with POS tag x. labelling Joint (p i , ) calls labelling Dep (p i , a) which performs action a on p i .


We use the standard split of CTB5 as described in (Duan et al., 2007)  Following Zhang and Clark (2008a), in the experiments, we also use a dictionary to limit the number of candidate tags for each word.

for a word which occurs more than M times in the training data, the tagger only considers the POS tags co-occurred with that word in the training data.


We explore the contribution of distributional information for purely knowledge-based word sense disambiguation.

we use a distributional thesaurus, computed from a large parsed corpus, for lexical expansion of context and sense information.


We propose a new method to bridge this lexical gap which is based on statistics collected from a large, unannotated background corpus.

we enrich the textual information from the context and the MRD with lexical expansions produced by a distributional thesaurus.


There are a number of errors in the gold standard annotations, and the methodology of the original task is different from what has subsequently become common.

not all of the target words have a corresponding entry in the sense inventory, and systems were originally expected to mark these "unassignable" senses as such.


We observe that the system had disproportionate difficulties with verbs, which constitute 35% of the incorrect disambiguations but only 26% of all target words in the corpus.

troublesome were verbs such as be, go, have, and do, which are often used as auxiliaries.


First, we present a Joint Topic Expression (JTE) model for modeling debate posts and use it to generate our unique dataset for studying accommodation in debates.

we analyze the phenomenon across agreeing/disagreeing debating pairs generated using our JTE model.


To perform these analyses, we need the right data.

we need to classify debate posts into those showing agreement and those showing disagreement.


The theory of communication accommodation in linguistics (Giles et al., 1991) hinges on the general observation that during conversations/communications both textual and spoken, people try to unconsciously converge to one another's communicative behavior.

there exists some coordination among conversers across a variety of dimensions like words, syntax, style, etc.


If case 1 holds, then accommodates to more than does to on  Before proceeding to analyze this interesting and fine-grained linguistic phenomenon of stylistic influence in debates, we need to statistically validate the existence of stylistic influence in online debates.

we are interested in answering the following question: In general, across various conversing pairs, is one of the users (former) forming the pair stylistically influencing (or causing the other user (latter) to accommodate) more than the extent to which he/she (the former user) is accommodating to him/her (the latter user)?


In this paper, we propose a more general approach to discourse structure prediction at the document level: (i) it performs a global search over the space of possible structures and optimizes a global criterion over the set of potential coherence relations; (ii) it can also take into account linguistically motivated constraints on the predicted structure.

our approach relies on the A* search algorithm, which is particularly well suited in allowing to capture constraints such as the so-called Right Frontier Constraint (or RFC), various versions of which have been a staple of theoretical linguistic approaches to discourse (Polanyi, 1988).


(2010) both rely on locally greedy methods, and in line with all previous works, treat attachment prediction and relation label prediction as independent problems.

they start by computing probabilities of attachments for adjacent pairs of EDUs and greedily select the highest scoring.


We assume the head-final order for dependency.

a non-final word always modifies a word on its right.


We begin with a first-order projective dependency model (Eisner, 1996;McDonald et al., 2005), which will be extended later.

we use a high-dimensional linear classifier.


We implement the online passive-aggressive algorithm (Crammer et al., 2006).

we use the variant named PA-I, with predictionbased updates (Crammer et al., 2006) and weight averaging (Collins, 2002).


This noun compound can be interpreted as a fusion of "shinka gēmu" (evolutionary game) and more prominent "gēmu riron" (game theory).

the non-head word (game) of the latter is modified by the grafting of the former.


Somewhat surprisingly, we found that among four paraphrase features, only c PARA (n j , j+1 S k ) (the argument's head word and the predicate's span) had a positive weight.

the argument span i S j was considered useless or even harmful by the model.


We are interested in embedding the m words in a k dimensional space, where k < n usually.

we are interested in estimating matrix A ∈ m×k , where the i th row, A i,: , represents the new embedding for the i th word.


While in principle it would be possible to produce the NNSE models directly on the co-occurrence data, here we chose to do this by using the SVD left-singular vectors as a stand-in for the full data.

this is our input representation matrix X ∈ m×n with m = 35560 words and n = 2000 input dimensions.


This might possibly be due to the fact that with a small number of available dimensions (e.g., k = 50), each dimension is used to represent multiple latent concepts, resulting in non-zeros values for larger number of entries in each column and thereby in the full matrix A.

each dimension acts as a mixture of latent concepts.


In this section, we evaluate how interpretable these representations are.

we would like to measure how coherent each of the dimensions of these representation are, i.e., given a representation A, we would like to  Boyd-Graber et al., 2009), word intrusion detection precision is used as the evaluation metric (higher precision implies higher interpretability).


we assume that they are built according to one of the following syntactic schemata of which the first four are very frequent and the last one is much less common.

we do not allow for prepositional phrases to occur within the terms (compare the results of the terminology extraction task described in (Marciniak and Mykowiecka, 2012)).


This is because of the product of n-gram precisions in the geometric mean of the precision component of BLEU: if some p n is zero, the whole product will be zero.

it is easy to see that BLEU will be zero for any hypothesis without 4-gram matches.


We therefore present in this paper a more generalized approach to joint inference via combinatorial factor constraints in factor graphs.

we describe a special-purpose combinatorial factor for constraining syntactic variables to a valid constituent bracketing, providing an efficient and flexible method for representing constituent syntax in graphical models.


For error mining however, what is needed is to identify attributes which predominantly associate with the FAIL class.

we need a metric which permits identifying attributes which leads to classes that are homogeneous in terms of FAIL instances rather than homogeneous in terms of either FAIL or PASS instances.


Such cases are illustrated in Figure 6 which show two views (one with part of speech tag only, the other with words and parts of speech tags) of the suspicion tree obtained after addressing the two main causes of errors identified in the previous section.

a rewrite rule was applied to convert the SR representation of possessive NPs to conform with the format expected by our realiser ((POSS) suspicious form); and the grammar was corrected to generate for NP coordination ((CC) suspicious form).


Noticeably, the suspicious form (CD) does not appear in the suspicion tree.

the tree highlights the fact that cardinals lead to generation failure in the contexts shown but not in all contexts.


Following (Gardent and Perez-Beltrachini, 2010), we therefore use not the initial FB-LTAG described in the previous section, but the FB-RTG grammar of derivation trees that can be derived from it.

the surface realisation algorithm first builds a derivation tree.


This is because at this stage of processing, the trees built bottom up for 'which fruit', 'John' and 'have' includes two NPs with LHS category N P S (Figure 6) while the r8 rule only requires one such NP.

for this rule, the completed daughter rule for which fruit will show up as a superfluous syntactic resource.


As a result, the intermediate structure shown below will not be computed.

while the global polarity filtering used in (Gardent and Kow, 2005)  Figure 7: Given the input tree eat(john, has), local polarity filtering filters out this intermediate structure because it cannot be completed given the input N-gram filtering using a Language Model.


In this paper we further develop two methods first introduced in (Visweswariah et al., 2010(Visweswariah et al., , 2011 with the aim to address specific issues arising in English↔German SMT.

we extend the parse-based reordering introduced for a variety of language pairs in (Visweswariah et al., 2010) by creating rules capable of capturing essential contextual triggers in the parse tree hierarchy.


We use data-driven syntactic reordering to mitigate some of the differences in word order mentioned above.

we investigate two methods of syntax-based reordering: (1) an   (Visweswariah et al., 2010), and (2) feature-based reordering model described in (Visweswariah et al., 2011).


Obviously, issues will arise for rule extraction given that these two sentences are composed quite differently.

the verb "applied" remains unaligned in this case thus introducing an error to the estimated target position for the corresponding set of node descendants, as described in Section 2.1.


The key difference between CRS and CKLD is that CRS tries to promote words which are important to all the topics within a category, while CKLD seeks words which are unique in terms of word usage in a category.

cRS is an intra-category measure, while cKLD is an inter-category measure.


First, we simplify the input features and classification methodology.

we use a support vector machine provisioned with only two features: dependency parse similarities of the time expression and the path from the event to time expressions.


This paper therefore addresses the question of how to empirically distinguish between analyses of individual phenomena, or between entire formal systems, on the basis of learnability in a down-stream unbounded dependency recovery task.

this paper describes a method for implementing syntactic analyses of various phenomena through automatic reannotation rules, which operate deterministically on a corpus like the Penn Treebank (Marcus et al., 1993) to produce a corpus with desired syntactic analyses.


A word sequence in CS is defined here as the correspondence of another sequence in TS if the words of one sequence are considered to represent the words in the other.

chinese-Tibetan BaseNP correspondence is an alignment at phrase-level.


Meanwhile, the proportion of weighted frequency of other words will decrease.

relevancy of the error Tibetan correspondence will decrease.


The key problem is how to get the head-phrase and how to extend it to a correct Tibetan BaseNP.

we decompose the identification of Tibetan BaseNP into headphrase extraction and head-phrase extension steps.


Unfortunately, the current version of Składnica contains sentences which were relatively unproblematic forŚwigra and for human annotators, i.e., it is skewed towards simple and short sentences.

coordination is underrepresented in general (1869 out of 8227 sentences, or 22.7%, contain conjunctions), and no cases of lexico-semantic coordination were found by the authors.


It must be stressed, however, that -although the details of the interaction of coordination with case assignment and other phenomena vary from language to language -the general mechanisms described in sections 4-7 seem to be applicable to any language.

coordination of unlikes has been by now reported for many languages, syntactic case assignment mechanisms which interact with coordination can be observed in Slavic, Baltic and Finno-Ugric languages (to limit ourselves to European languages), and lexico-semantic coordination has been described for French, Hungarian, Romanian and a number of Slavic languages.


It has long been established that there is a correlation between dialog behavior of a discourse participant and how influential he or she is perceived to be by the other discourse participants (Bales et al., 1951;Scherer, 1979;Brook and Ng, 1986;Ng et al., 1993Ng et al., , 1995.

factors such as frequency of contribution, proportion of turns, and number of successful interruptions have been identified as being important indicators of influence.


But only 12 of them (10.6%) were perceived to have situational power by our annotator.


The 12 of those who also had hierarchical power amounts to only 11.2% of them.

you don't have to be the boss to be bossy.


Fung (2000) extracted word contexts from a comparable corpus, and calculated the similarity between word contexts via an online dictionary.

she analyzed the impact of polysemous words, Chinese tokenization and English morphological information.


(2008) proposed a generative model to construct lexicons for multiple language pairs, including English-Chinese, via canonical correlation analysis, which effectively explores monolingual lexicons in terms of latent matching.

for BLC without any external lexicon, Fung (1995) focused on context heterogeneity in Chinese and English languages, which measures how productive the context of a word is, instead of its absolute occurrence frequency.


She suggested that bilingual translation words tend to share similar context heterogeneity in non-parallel corpora.

she calculated the similarity between two bilingual words using the ratios of unique words in the right and left contexts.


For corpus pre-processing, we use the Stanford POS tagger (Toutanova and Manning, 2000) and syntactic parser (Marneffe et al., 2006) to generate the POS and dependency information for each sentence in both Chinese and English corpora.

english words are transformed to their respective lemmas using the TreeTagger package (Helmut, 1994).


In this paper, we adopt a small scale one.

we use the GIZA++ package (Och and Ney, 2000) to extract the most frequently occurring 1000 word pairs as the bilingual seed lexicon (denoted as L s ) and the subsequent 500 noun pairs (denoted as LN t ) as the primary bilingual test lexicon.


(2009) extracted the words from the dependency tree with a fixed window size of ±2 as the context.

given a word w in a sentence, all the words corresponding to its immediate parent (-1), immediate children (+1), grandparent (-2) and grandchildren (+2) are extracted as features to constitute a context vector.


That is, given a word w in a sentence, all the words corresponding to its immediate parent (-1), immediate children (+1), grandparent (-2) and grandchildren (+2) are extracted as features to constitute a context vector.

each feature in the vector is weighted by its point-wise mutual information (PMI) with the word w, defined as: where N(w, c) is the co-occurrence frequency of word w and its context word c, N(w) and N(c) are the occurrence frequencies of the words w and c respectively, N is the number of all word occurrences in the corpus.


From the figure we can see that while some mappings, such as nsubj (EN) to nsubj (CN) and dobj (EN) to dobj (CN), capture common grammatical relationships in both languages, others, such as poss (EN) to assmod (CN), indicate the differences across the two languages.

interesting is that nn (CN) can map to four relationships, namely nn, amod, prep_of and poss (EN) while nn (EN) has only one correspondence nn (CN).


We argue that this may not be optimal since different mappings may have different contributions to BLC.

different mappings should have different weights to exploit such difference.


All the 4 combinations exhibit similar trends.

with the shrinkage of the mappings, the performance first slightly increases, then peaks at some iteration, afterwards decreases slowly, and finally drops significantly.


This suggests that the former two are more discrimitive than the latter two in dependency mapping-based BLC, though the latter two occur more frequently as indicated in Table 2.

the meaning of a noun depends more on its conjuncts and possessive/associative modifiers to a certain extent than on its prepositional phrases or noun premodifers.


The reason is that the dependency mapping context between "profit" and "利润", such as "实现_dobj" (realize_dobj) and "成本_conj" (cost_conj) etc., is more evidential than that between "profit" and "企业".

the dependency mapping context contains more accurate bilingual corresponding words and ignores noisy ones than the dependent word context, thus leading to better performance for BLC.


nouns, verbs and adjectives) in a wide frequency range.

for the DM and DM_AP methods, the automatic mappings and their weights for nouns are the same as those in Table 2 while for verbs and adjectives, the mappings are first automatically generated and then filtered using the ablation tests with their weights learned on the development sets LV d and LJ d respectively.


Multi-word expressions (MWEs) nowadays still pose problems to most language technology and applications, e.g information retrieval, text mining, semantic web.

they impact greatly on the performance of Machine Translation systems and automatic dictionary compilation.


This is done by using the same variance analysis of the distribution in the sets of patterns described above with the aim of selecting more than one relevant pattern.

the presence of outliers is evaluated in terms of standard deviation above the mean.


We thank PANACEA reviewers and the COLING anonymous reviewers for their comments and incentives to improve.

we want to thank Fabio Affé who helped us with the final modifications to the java code and with the deployment in Soaplab.


Motivated by the inability of a range of direct clustering approaches to improve over the current leading algorithm, we propose a mixture of experts approach.

we tackle the difficult challenge of producing a diverse collection of useful tagging experts, which can then be aggregated into a final high-quality tagging.


Constructing multiple experts using the DPMM framework is simply done by varying the concentration parameter α.

each expert is associated with a different α value, resulting in a different clustering.


The first 2S features correspond to the appearance of a POS tag in the leftmost/rightmost position of a constituent labeled by the represented BMM category.


The structural rules will have uniform probability, and the lexical rules will have a portion of the probability mass relative to their cooccurrence in the training corpus.

we will initialize a stochastic bracketing FSTG such that: where c e/ f is the cooccurrence count for the biterminal e/ f ∈ To calculate the expectations for the expectation maximization, we will parse the training corpus with the grammar we have, using an adaptation of the parsing algorithm described in Saers et al.


Although this information can be (and has been) leveraged for guiding users through a faceted search experience, we focus in this paper on the extraction of salient questions from unstructured data.

we aim to leverage user-generated comments and descriptions of database records as the source of information from which we will guide search users.


The cost in the lexicon of spelling errors is obtained based on the corresponding correct form.

the model is able to decode unseen spelling errors, if correct candidates for the misspelled word exist in the correct lexicon.


In many error cases, incorrect POS tagging is due to a failure in spelling error correction.

when misspelled words are corrected successfully, the result of POS tagging is also correct.


In this paper, we present a multi-stage text classification system for assessing psychological status of individuals based on their text postings on online web forums.

our system combines state-of-the-art NLP and machine learning techniques to: (1) extract fine-grained psychological distress indicators/labels derived from Diagnostic and Statistical Manual of Mental Disorders (DSM) IV (American Psychiatric Association, 2000), and (2) assesses the severity of distress that can be used to triage individuals who should seek clinical help.


We show that these rationales can be effectively used to improve multi-label classification accuracy.

we observe a relative improvement of 14.6% over using plain text features.


The evaluation phase corresponds to the test of the termination criteria: convergence of the score of the best individual.

if the score of the best individual remains the same for a number of generations (STH), the algorithm terminates.


Otherwise, the temperature is lowered to T • ClR.

the more cycles it takes for the algorithm to converge, the lower the probability to accept lower score configurations: this guarantees an eventual convergence.


Our experiments show that for three of the VSS's there is one alternative that is more learnable over all settings.

training any of the five parsers on an annotation scheme that uses the more learnable alternative results in higher parsing performance.


Second, we show that this effect can be used for designing syntactic annotation schemes.

our findings indicate that future dependency schemes should use (a) prepositions as heads of PPs (b) conjuncts as heads of coordination structures and (c) nouns (and not their determiners) as heads of NPs.


selecting all three of the empirically preferred annotations results in a gain similar to the sum of the average gains in the individual structures.


We showed that the gain from selecting the empirically preferred annotations is both substantial (error reduction of up to 19.8%) and additive.

selecting all three results in an even more accurate parser.


Distributional clusters are made up of words that appear in similar contexts to each other, whereas selectors are words which show up in the specific context of a single instance.

selectors are instance-specific while distributional clusters are created based on observing many instances of context.


Searching based on all sequences can be expensive in terms of disk IO, so the sequences are sorted such that the process can be halted once enough selectors have been found.

we define four criteria of informative value for a given sequence seq: where sels(seq) is the set of selectors found when searching with sequence seq.


Supervised classifiers are trained with and without utilization of selectors and we record a simple accuracy of |cor r ec t_inst ances| |al l_inst ances| * 100 of the testing data.

3 we use support vector classifiers implemented with Scikit-learn (Pedregosa et al., 2011) with a radial basis kernel and other parameters set via 5-fold crossvalidation over the training set.


In this section, we discuss this improvement by examining the values of features extracted for instances in the SemEval-2007 experimental corpus.

we endeavor to show that selectors contribute robustness to the WSD feature space through an abstraction of context that distinguishes senses of words.


The SVM technique (Joachims, 1999;Vapnik, 1995) takes a strategy that maximizes the margin between the critical samples and the separating hyperplane.

sVMs achieve high generalization even with training data of a very high dimension.


The system needed a detailed group level analysis to resolve the ambiguities between adjective and noun, main verb and auxiliary verb or demonstrative and pronoun.

a granular group level analysis was needed that made use of the morphotactical arrangement both within a word form and in between words.


In the name of efficiency, we apply several rules to limit the number of pairs of mentions that our algorithm considers.

(we run our Kruskal's-like algorithm on a sparse graph rather than a complete graph with O(n 2 ) edges.)


Each greedy decision can rely on all of the information contained in the coreference chains built so far.

we can rely on features that attempt to capture agreement between entire clusters.


After some experimentation on development data, we decided to select the local cluster vector which results in the highest overall local score.

we pick the single local vector that scores highest under w. This resembles single-link clustering.


One index of Task Control is the number of directives (done as statements or questions) made by each participant as a percentage of all directives in discourse, known as Directive Index (DI).

a participant who tells others what to do (whether overtly or more subtly) is attempting to control the task that the group is performing.


Another measure is the degree to which a participant is a "center hub" of the communication within the group.

someone whom most others direct their comments to as well as whose topics are most widely cited by others.


Here, the increased data size does seem to lead to some small improvements in performance  Table 3: Cross-validation results for all systems on each corpus; accuracy in % for some features, but not all.

some of the binary features perform worse on the TOEFL11-Big corpus compared to the smaller TOEFL11 corpus.


The results generally show lower performance than the cross-validation experiments.

the system trained on the ICLE-NLI data set does not generalize at all to the TOEFL7 data set.


Our experiments are based on the same data sets as we have used for our baseline approaches presented in section 1.

we used the portion of the parallel SETimes corpus containing Bosnian, Croatian and Serbian with its 2.7 million words per language.


In this work, we focus on sentence pairs connected by an implicit relation and belonging to the EXPANSION class.

we are interested in the relations in the EXPANSION class marked as Restatement, because the way in which such relation is defined shows high similarity with the textual entailment relation.


According to the UID hypothesis, we would expect that causal relations which contain an IC verb in their first argument (Arg1) would be expressed without an explicit connective more often, as the reason relation is predictable.

as our third hypothesis, we investigate whether: 4. backward causal relations that contain an IC verb (which already marks causality) in their first argument are implicit more often than those that contain a non-IC verb.


Sentences with inappropriately placed adversative connectives were also judged as less coherent than ones with inappropriately placed causal or additive connectives.

there was no difference in processing disruption or coherency judgment between inappropriately used additive and causal connectives, which is why Murray attributes the difference between these connectors and adversative ones to the underlying continuity or discontinuity of the described event.


The main motivation behind our study of causal relations comes from Sanders's cognitive theory of discourse representation.

the causality-by-default hypothesis states: "because experienced readers aim at building the most informative representation, they start out assuming the relation between two consecutive sentences is a causal relation" - Sanders (2005) .


(2011) finds that a small discourse consisting of three sentences was easier to process when the sentences were causally related.

a larger N400 (an EEG signal which typically indicates semantic anomalies) was found when sentences were irrelevant.


An analysis of simple frequencies of total occurrences would however be rather limited, as there are a number of possible confounds.

we would like to be able to test whether there is any evidence for people generating expectations of upcoming discourse relations, as argued by the continuity and causality literature.


By doing this for each translation candidate t (k) , we construct a probability distribution of possible translations of s j based on the n query translations.

if source token s j is aligned to (i.e., translated as) t i in the k th best translation, the value ℓ(t (k) |s) is added to its probability mass, producing the formula for P r nbest : where ϕ is the normalization factor.


In the case of the Arabic collection, we observe a strictly decreasing trend for the MAP scores as λ 2 decreases, and the best results are obtained when λ 1 is 0 and λ 2 is 1.0 (call the condition with the best MAP score E).

the interpolation yields a maximum 0.3058 MAP when it was based entirely on P r phrase , ignoring distributions P r token and P r nbest .


Our method uses a co-occurrence model augmented with a factorization algorithm to cluster verbs from a large corpus.

we use non-negative tensor factorization (NTF) (Shashua and Hazan, 2005), a generalization of matrix factorization that enables us to capture latent structure from multi-way co-occurrence frequencies.


Finally, UserC proposes an independent answer again to the original question (Dialogue Act = Answer-answer).

we explore features extracted from the thread discourse structure which can be used to help classify the Solvedness of threads.


(2006) showed that dialogue acts can be used to classify student online discussions in web-enhanced courses.

they use dialogue acts to identify discussion threads that may have unanswered questions and need the attention of an instructor.


In this work, we introduce new features to improve the performance.

syntactic dependency features, semantic class features and distance features are added to the baseline models.


(2) Collect HTML files of 900 web pages from the retrieval results for each topic.

the first 300 pages in the retrieval results from forum sites, the first 300 pages from blogs and the first 300 pages from general sites.


, the BIO tags are encoded in the following way: We employ the linear chain CRFs (Lafferty et al., 2001) as our learning model for BIO tagging.

we use CRF++ (version 0.54) implementation by Taku Kudo.


Therefore, we consider that the opinion holder identification consists of a classification task and an information extraction task.

the evaluation holder is identified in two steps in CEIA: (1) use linear kernel support vector machines (SVMs) to determine whether the evaluation holder is author or not-author; (2) if the evaluation holder is not the author, then use a CRF tagging    A typical approach for sentiment classification is to use supervised machine learning algorithms with bag-of-words as features (Pang et al., 2002).


For convenience we assume that r i = {δ} for i < 1 ∨ i > n. Definition 1 can be easily modified to take those constraints into account.

the model assigns probability 0 to all label sequences which are inconsistent with the constraints imposed by r: The normalization factor within the constrained context is defined as We describe here two methods of inference using CCRFs: finding the most probable label sequence given the sentence and assigning marginal probabilities to individual labels from restricted sets.


For example, if we ignore the problem of unknown words completely, we will be constrained to use methods which manage to handle adjacent words with more than 1000 possible tags at the stage of disambiguation.

the method described in section 4 would not be able to handle such cases in an efficient way.


The comparison of Concraft and WCRFT doesn't clearly show which tagging system would perform better if we used both of them in combination with the same guesser and this question should be further investigated.

we plan to evaluate the guessing and the disambiguation components of the Concraft system separately.


Mining content from the Web to support human-computer interaction applications (e.g., question answering (Radev et al., 2001;Clarke et al., 2001;Yao et al., 2012), interactive question answering (Wong et al., 2011), conversational systems (Huang et al., 2007;Wu et al., 2008;Shibata et al., 2009)) has been investigated since the advent of the read-write Web.

the practice of mining the Web to alleviate the bottleneck of conversational content acquisition is increasing in popularity.


If the term is absent from the context, the new term and its weight are added to the context.

recurrence is an important factor for a term to maintain its prominence in the context, where the more times a term occurs in the conversation, the more significant it will become through the compounding of its weights.


The other 26% (43 utterances) and 64% (108 utterances) are judged as very natural and natural, respectively.

the chart is left-skewed, with the majority (90%) of the judgements tending towards the very natural end of the scale, with both the mode and median for all 11 conversations being at level 3, which is natural.


The contributions from stem and lemma factors are less than 1.0%.

it is not necessary to use both stem and lemma because they are very similar and obviously do not complement each other.


(2008) then proposed to generate resources and tools for new languages (Spanish and Romanian) using machine translation and cross-lingual annotation projections.

they used machine translation to transfer the manually or automatically annotated training data from the source language (English) into the target languages, and projected the subjectivity annotations of the transferred data across language correspondingly.


(2010) proposed to combine multiple language spaces altogether in an expanded feature space.

they combined the original English feature vector of an instance and its translated feature vectors in different target languages together into one feature vector, and then used the training instances expressed in this expanded feature space to train multilingual subjectivity classifiers.


To address multilingual subjectivity analysis in an AdaBoost framework, we propose to integrate multi-view learning into the AdaBoost.

we develop two approaches using different multi-view learning strategies: a multi-view majority voting AdaBoost (MVAB1) and a multi-view weighted voting AdaBoost (MVAB2).


Similar to Multi-view Majority Voting Ad-aBoost, at each round t of Multi-view Weighted Voting AdaBoost, we separately train a set of single view classifiers {h t v } over the training instances with a weighted distribution D t , one for each language (view) v. But instead of taking a majority vote, we set the combination base classifier h t as a linear combination of the single view classifiers with a set of weight parameters where In order to obtain the weight parameters, we train this linear combination model on the weighted training set, using the outputs of the single-view classifiers as features.

we minimize the following weighted least square loss on the training instances to to obtain the β values: With the obtained base classifier h t in Eq.


The feature set used for the CRF POS tagger is presented in Table 1.


From Figure 2, we can see that by increasing the number of labeled training data, DDN can gain more improvements in accuracy compared with Baseline, SCL and HMM.

ddN increases accuracy by 2.4% compared with Baseline, by 1.3% compared with SCL, and by 0.6% compared with HMM when the labeled training data is 1,000.


According to Figure 3, by increasing the number of labeled training data, DDN can gain more improvements in term of F1 measure comparing to Baseline, SCL and HMM.

ddN increases the F1 by 2.52% comparing with Baseline, by 1.91% comparing with SCL, and by 0.74% comparing with HMM when the number of labeled training sentences is 1,000.


Furthermore, it is trained from sentence pairs without word alignments in an unsupervised fashion.

: • We approximate the exact conditional log-likelihood objective inspired by contrastive estimation (Smith and Eisner, 2005) as the optimization of the exact objective is very expensive.


Although |L| can be larger than 3 (a hypercube is defined in that case), we still call it cube for consistency.

given a point p in the cube, multiplication operator ⊗ constructs a hypothesis by there are two types of cubes: word cube and span cube.


To cast the correction problem as a classification problem, the system has to know, a priori, what are the set of possible corrections for an error.

the system needs to pre-define a confusion set for each error type.


To simulates an intension based learner, we build a model of word similarity metrics from processing standard English text.

we build distributional models in which the similarities of words are calculated from a comparison of the contexts they appear in (Pereira et al., 1993;Lin, 1998;Lee, 1999).


To evaluate the confusion sets' qualities, we examine how they impact the performance of an end-to-end grammar error correction (GEC) system.

we train a separate classifier for each preposition using only training examples that are covered by the confusion set, a setup similar to the NegL1 system as described in (Rozovskaya & Roth, 2010a).


the filtering we discussed above is only applied on the training data.


, C N in the corpus, note that by grouping the samples with same contexts together, we may rewrite formula 1 as: Secondly, for a certain V , the optimal function f which minimizes the cost function should .

f should map context C k to the centroid of the word choice vectors in group D k .


Besides, a number of rules were used to reduce the errors.

event recognition in this system includes two types of processing: lexical, using specific dictionaries and contextual, using a number of rules.


We use the normalized weight for edge defined by equation (1) or(2) for the element m ij .

m ij =e(p i , n j ) if there is an edge between program node p i and noun node n j , m ij =e(n i , n j ) if there is an edge between noun node p i and noun node n j , and m ij =0 if there is no edge between node i and node j.


The POS n-grams (n = 2, 3, 4) in the sentence are extracted, and used as in the bag-of-words approach.

these POS n-grams are treated like words and considered as the features for classification.


we divide the entire translation process into 3 steps: (1) PAS acquisition: perform semantic role labeling (SRL) on the input sentences to achieve their PASs, i.e., source-side PASs.


However, the manually annotated corpus is always too inadequate to fully display the strengths of tree-based models.

traditional tree-based systems can not work at all for language pairs without any syntactic resource, which has greatly restricted their application.


The alternative tree structures depend only on the word alignment without utilizing any syntactic resource or linguistic parser (see FIGURE 1(b) for illustration).

the entire process of inducing such tree structures for tree-based translation models is summarized as follows: 1.


In addition to the new notion of benefiting from the auto-generated instances related with the queried name, our approach further benefits from the manually labeled data related to other names.

the learned linker generalizes on the labeled data sets related to both queried and other names by exploiting the inherent predictive structure shared by these two data sets.


a document d q with a mention m q ) and its associated KB candidates C q ={c 1 ,..,c N } generated in Section 3, and our goal is to select the correct KB entry e from the set C q .

let φ q (q,c i ) be a score function reflecting the likelihood that the candidate c i is the correct KB entry for q.


In order to learn the predictive structure Θ shared by M and A q , we need to (a) select relevant prediction problems (i.e.

relevant names) from M. they should share a certain predictive structure with the target problem; (b) select useful features from the feature set shown in Table 2.


This is because "Communist Party of China" is likely related with the topic "politics", but "the city of New York" does not have such particular topic.

shared structure Θ between different names may depend on feature types, and in that case, seeking Θ for each of feature groups (CF, SeF, SuF and GS in Table 2) separately may be more effective.


Compared with the existing dependency relation-based question answering passage retrieval model PRM, our method outperforms PRM in the above five evaluation methods.

we only use dependency path length as a bridge to capture the relevance between two terms, which is a simple, stable and efficient way to use deep parsing, and get better performance.


If all prefixes in all possible divisions of a word count as stems, then as computed from the WSJ corpus, the top three suffixes with the highest contextual diversity will be '-s', '-d' and '-e', two of which do not comply with common sense of morphological suffixes in English.

for acquiring suffixes, we want to compute their contextual diversity according to properly justified stems only.


This paper addresses this problem by making innovative use of user behavior information embodied in query sessions.

we learn paraphrase patterns from the search log sessions with a classification framework, in which three types of user behavior features are exploited besides the conventional features.


Paraphrases have been shown to be useful in plenty of areas, such as machine translation (MT) (Callison-Burch et al., 2006;Madnani et al., 2007;Marton et al., 2009), question answering (QA) (Lin and Pantel, 2001; Ravichandran and Hovy, 2002;Duboue and Chu-Carroll, 2006;Riezler et al., 2007), and web search (Zukerman and Raskutti, 2002).

the capability of paraphrasing is essential in web search, since in many cases the user queries need to be paraphrased so as to improve the quality of the search results.


The results show that the classification based approach is effective in paraphrase recognition.

the user behavior features can significantly enhance the classification performance.


It is therefore less possible for her to paraphrase the query any more.

users' clicking and browsing behaviors are good indicators for recognizing paraphrases.


As mentioned above, Zhao-10 makes use of the click-through relationship between queries and clicked document titles from query logs.

zhao-10 learns paraphrase patterns in two steps.


Moreover, our parser outperforms previously reported shift-reduce constituency parsers while maintaining efficiency.

we make the following contributions in this paper: • We propose a set of novel features for better shift-reduce constituency parsing that is based on lexical dependencies obtained from large-scale auto-parsed data; • We empirically compare two different sources for obtaining lexical dependencies: humanlabeled treebank data and large-scale auto-parsed data respectively, and show the superiority of using auto-parsed data (Section 5.1); • We empirically analyze major sources of shift-reduce parsing errors (Section 3.1) and verify the effectiveness of new features in resolving shift-reduce action conflicts (Section 5.6.2); We use the beam-search shift-reduce parser (Zhang and Clark, 2009) as the baseline system in this paper.


Formally, each state in the sequence is denoted by a tuple 〈S, Q〉, where S is a stack containing partial parses and Q is a queue of word-POS pairs that remain unprocessed.


Here the switch L/R indicates the head among the three words.

the symbol L specifies w 1 to be the head and the symbol R designates w 3 to be the head.


After extracting all lexical dependencies, we group bigram and trigram lexical dependencies separately into three categories according to their frequencies.

if a dependency relation is among top-10% most frequent records, then it receives the group tag High Frequency (HF); else if it is in top-20%, then we use the tag Middle Frequency (MF); else we use the tag Low Frequency (LF).


For Chinese experiments, we used Chinese Treebank (CTB) version 5.1 (Xue et al., 2005) as labeled data.

articles 001-270 and 440-1151 were used as training data, articles 271-300 were held out for performance evaluation, and articles 301-325 were used as development data.


We conducted necessary preprocessing on English and Chinese unlabeled data before they were fed to the baseline parser for automatic parsing.

we applied OpenNLP for English sentence boundary detection and tokenization.


As the results show, both bigram and trigram dependency features have positive effect on the parsing accuracy.

on the whole test set the overall improvement over the baseline parser is +0.9% in F1-score, where bigram dependency features contribute an absolute 0.6% improvement and trigram dependency features further improve the performance by 0.3% over the results of using bigram dependency features.


There has been great interest in unsupervised learning among NLP researchers due to the availability of large amounts of unprocessed text through the Web, newswire providers, scientific repositories and other sources in contrast to the onerous requirements of creating task-specific manually annotated data for training supervised analysers.

relevant to our work is the field of topic modelling, where Bayesian latent-variable models are used to induce meaningful generalisations from observations of co-occurrences.


Dynamic content features are designed to capture the semantic relationships between each turn and its precedent and subsequent turns.

we refer to relations such as lexical and topical cohesion, question-answer relationship, and the appearance of general cues that may anticipate relevant bits of information in the subsequent turn.


Call-based features are meant to capture contextual information at the call level.

x-C features include information about where, when and for what reason a call is made, under the intuition that calls made, for example, during working hours may have different noteworthy information than calls made in the weekend.


This result gives empirical evidence that these two sets of features convey complementary information that is relevant for the task at hand.

the same words can carry different relevance depending on the contextual information of the conversation.


Interpretations of rewards are very culturally sensitive.

for example, a comment such as "disgusting" would have a good chance of being interpreted as a positive reward when it was made as a comment to a user-generated contribution on the website DeviantArt.com.


Serialization methods also perform better than the tf-idf baseline for induced topics, as shown in Figure  3 and Table 9.

reply and Discourse markers, which were far from the best for Snowpiercer, serve well for other topics such as Movie in general, Politics, and Gender/relationships.


Thus, rather than having to take a pipeline approach, we are able to develop an optimization framework which exploits these interdependencies to jointly resolve the ambiguities.

we develop local detectors to analyze the four individual components of gfbf events, (Q2)-(Q5) above.


There are several parameters used in the paper, we tune these parameters on the validation set.

we set the number of category-specific topics per category and the number of shared topics in GNMFNC as (K s , K p ) = {(5, 2), (10, 4), (20, 8), (40, 16), (80, 32)}, resulting in K = {57, 114, 228, 456, 912} total number of topics.


For regularization parameters α p and β l , it is difficult to directly tune on the validation set, we present an alternative way by adding a common factor a to look at the objective function of optimization problem in equation 3on the training data.

we set α p = a Ks×Kp and β l = a Kp×K l .


In this experiment, we present the experimental results for question retrieval on the test data set.

for our proposed GNMFNC, we combine the latent topic matching scores with the term matching scores given by BM25 and LM, denoted as "BM25+GNMFNC" and "LM+GNMFNC".


In this paper, we define SRC as a multi-objective optimization (MOO) problem to take advantage of most recent works in clustering.

we define two objective functions (compactness and separability), which are simultaneously optimized using a MOO-based simulated annealing technique called AMOSA.


As far as we know, within text applications, (Morik et al., 2012) is the first work, which formulates text clustering a multi-objective optimization problem.

they express desired properties of frequent termset clustering in terms of multiple conflicting objective functions.


As we follow an endogenous approach, only the information returned by a search engine is used.

we only deal with web snippets and each one is represented as a word feature vector.


In this paper, we propose to use the F b 3 -measure (Amigó et al., 2009) to explore the Pareto front.

f b 3 has been defined to evaluate cluster homogeneity, completeness, rag-bag and size-vsquantity constraints.


Overall results are shown in  Results show that for MORESQUE, MOO-clus obtains the highest F b 3 value for p=5.

performance increases for higher values of p. For ODP-239, best results are reported for p=4, but evidence less sensitivity to the number of words in the centroids.


Descriptive queries such as "roasted chiles in Santa Fe" or "kid-friendly Caribbean resorts" pose a significant challenge to local search systems, as the information in the local index does not typically include terms that match the user's query.

the system may know businesses in Santa Fe, but not whether they sell roasted chiles.


To prevent a single prolific user from dominating the representation of a place, we estimate the term co-occurrence with the user frequency.

the term counts are the number of people who used a term in a place, rather than the number of times a term was applied.


We consider the tag accuracy to be proportion of "good" tags accounted for by a single data source.

for Twitter, it is the number of "good" Twitter tags, divided by the total number of "good" tags, whereas the accuracy of the Web tags is the number of "good" tags derived from the web, divided by the total number of "good" tags.


Since the set of queries consists of the entity name plus attributes from the index such as the location and the category information, the resulting precision from search just on the entity metadata itself shows the degree to which the bias in the data accounts for the results.

if you have the correct entity name, location and category, just searching for a business with matching metadata gives a precision at rank one of 0.595 (0.618 for Brazil).


A Bayesian interpretation of the above model is to select between the n-gram and the class-based model with probabilities α and 1 − α, respectively.

for each n- , we simply flip an α-biased coin to decide on one of the two models.


In this paper, we propose to leverage morphological knowledge to address this problem.

we introduce the morphological knowledge as both additional input representation and auxiliary supervision to the neural network framework.


To deal with the problem, neural network models have been widely applied to obtain word representations.

they usually take the 1-of-v representations as the word input vectors in the neural networks, and learn new distributed word representations in a low-dimensional continuous embedding space.


In this paper, we propose a novel neural network architecture that can leverage morphological knowledge to obtaining high-quality word embeddings.

we first segment the words in the training data into morphemes, and then employ the 1-of-v representations of both the words and their morphemes as the input to the neural network models.


In addition, we propose to use morphological information as auxiliary supervision.

in the output layer of the neural network architecture, we predict both the words and their corresponding morphemes simultaneously.


In each sliding window, the model aims to predict the central word using the surrounding words as the input.

the input words are represented in the 1-of-v format.


On the input side, we segment the words into morphemes and put both the words and the morphemes as input.

the vocabulary for the 1-of-v representation contains both words and morphemes.


In our model, we use negative sampling discussed in (Mikolov et al., 2013b) to speed up the computation.

we random select k negative samples u 1 , u 2 , • • • , u k for each prediction target (word or morpheme).


We did the same data pre-processing as csmRNN did.

we removed all non-Roman characters and mapped all digits to zero.


When using the C&W embeddings as the initialization, the performance of MorphemeCBOW* is also comparable with that of csmRNN.

if we do not use any pre-trained embeddings to initialize our mode, it performed the best (32.88%), and it even beats the best performance of csmRNN with initializations (31.77%) 8 .


In this paper, we introduce a much more efficient method for learning multi embedding vectors for polysemous words.

we first propose to model word polysemy from a probabilistic perspective and integrate it with the highly efficient continuous Skip-Gram model.


To address this problem, some recent efforts, such as (Reisinger andMooney, 2010) (Huang et al., 2012), have investigated how to obtain multi embedding vectors for the respective different prototypes of a polysemous word.

these works usually take a two-step approach: they first train single prototype word representations through a multi-layer neural network with the assumption that one word only yields single word embedding; then, they identify multi word embeddings for each polysemous word by clustering all its context window features, which are usually computed as the average of single prototype embeddings of its neighboring words in the context window.


In this section, we introduce our algorithm for learning multi-prototype embeddings in details.

since our new model is based on the continuous Skip-Gram model, we first make a brief introduction to the Skip-Gram model.


In contrast to the conventional ways of using context words to predict the next word or the central word, the Skip-Gram model (Mikolov et al., 2013b) aims to leverage the central word to predict its context words.

assuming that the central word is w I and one of its neighboring word is w O , P(w O |w I ) is modeled in the following way: where W denotes the dictionary consisting of all words, U w ∈ R d and V w ∈ R d represent the d-dimensional 'output' and 'input' embedding vectors of word w, respectively.


Word w's embedding vectors are denoted as V w ∈ R d×N w .

suppose there are M word pairs for training: where all the inputs words (i.e., word w) are the same, and the set of output words to be predicted are denoted as X are M surrounding words of w in the training corpus.


However, if we take the Hierarchical Softmax Tree form as shown in Equation 4to model P(w m |h m = i, w), and perform only one step gradient ascent in M-Step, the aforementioned two expensive operations in M-Step will be avoided by leveraging the pre-computed results in the E-Step.

since the gradient of the function f (x) = log ς (x) is given by f (x) = 1 − ς (x), the sigmoid values computed in the E-Step to obtain P(w m |h m = i, w) (i.e.


In this section, we will present our experimental settings and results.

we first describe the data collection and the training configuration we used in the experiments; then, we conduct a qualitative case study followed by quantitative evaluation results on a public word similarity task to demonstrate the performance of our proposed model.


Dataset: To make a fair comparison with the state-of-the-art methods, we employ a publicly available dataset, which is used in (Huang et al., 2012), to train word embeddings in our experiments.

this training corpus is a snapshot of Wikipedia at April, 2010(Shaoul, 2010, which contains about 990 million tokens.


During the training process, we used the same strategy to set the learning rate as what Word2Vec did.

we set the initial learning rate to 0.025 and diminished the value linearly along with the increasing number of training words.


If we want to predict the dependency structure of this sentence we need to make several decisions.

the parser would need to decide (1) Does electronic modify devices?


The simplest way of inducing a bilexical operator is to learn it from a training corpus.

assuming that we are given some data annotated with a linguistic relation between a modifier and a head (e.g.


We want this model to perform well on all head-modifier pairs.

we will test the performance of the model on heads that do not appear in D. We assume that we are given access to a distributional representation function φ : V → R n , where φ(x) is the n-dimensional representation of x.


Update W t+0.5 to take into account the regularization penalty r(W ), by solving For the regularizers we consider, this step is solved using the proximal operator associated with the regularizer.

: • For 1 it is a simple thresholding: • For 2 it is a simple scaling: • For nuclear-norm, perform SVD thresholding.


We also induce lexical embeddings, but in our case we employ supervision.

we follow a semi-supervised paradigm for learning representations.


The challenges are developing effective feature representation of phrases and obtaining training data with minor manual annotations for building the sentiment classifier.

we develop a dedicated neural architecture and integrate the sentiment information of text (e.g.


Our method contains two part: (1) a representation learning algorithm to effectively learn the continuous representation of phrases, which are used as features for phrase-level sentiment classification, (2) a seed expansion algorithm that enlarge a small list of sentiment seeds to collect training data for building the phrase-level classifier.

we learn sentiment-specific phrase embedding (SSPE), which is a low-dimensional, dense and real-valued vector, by encoding the sentiment information and Learning Algorithm Figure 1: The representation learning approach for building Twitter-specific sentiment lexicon.


We used WEKA 5 , which is a tool that includes (among other utilities) a collection of machinelearning algorithms that can be used for classification tasks.

we used a SVM-based approach because it is a well-founded formalism, that has been successfully used in many classification problems.


(1994) used stochastic grammars for CSL.

they applied stochastic Finite State Transducers (FST) for recognizing constituent annotations.


Syntactic Tree Kernel (K1), also known as a subset tree kernel (Collins and Duffy, 2002), maps objects in the space of all possible tree fragments constrained by the rule that the sibling nodes cannot be separated from their parents.

substructures are composed of atomic building blocks corresponding to nodes, along with all of their direct children.


It generates all possible connected tree fragments, e.g., sibling nodes can be also separated and be part of different tree fragments.

a fragment is any possible tree path from whose nodes other tree paths can depart.


Figure 1 (left) shows the graphical representation of CTR, which combines the merits of traditional collaborative filtering and probabilistic topic modeling.

the key mechanism of CTR is that using topic vectors learned from LDA (Blei et al., 2003) jointly controls the prior distribution of latent items in original matrix factorization process of CF.


Our model can handle all these relations in the proposed framework, CTR-MGF.

the generative process of CTR-MGF for heterogeneous networks is listed as follows: , multivariate Gauss distribution and set the item latent vector as -For each social graph specific feature m * draw graph factor-specific latent feature vector -draw the shared latent user vector among multiple social graphs In the above generative process, the joint likelihood of data, i.e., R, Q k=1,...,K , W , and the latent factors U, V, S k=1,...,K under the full model is: For learning the parameters, we develop an EM-style algorithm similar to CTR.


1: We employ coordinate ascent (CA) approach alternatively optimizing latent factor variables u i , v j , s k=1,2,...,K m and the simplex variables θ j as topic proportions.

the following update rules in CA are obtained by setting the derivative of L with respect to u i , v j , and s k=1,2,...,K m to zero.


Different from the graph sharing mechanism presented in last section, we relax the restriction that all users have the same representation.

we assume each latent user has multiple sub-graph specific representations.


For all CTR-based methods, we set a = 1, b = 0.01, λ v = 0.1.


Figure 4 clearly proves the effectiveness of our weak consensus modeling.

we can see that 0.0 seemingly means that the first graph is not selected due to the weight of the first graph T 1 = 0.0 and that of second graph T 2 = 1 (fully selected).


Figure 5 shows Our-Heter (N) can achieve improved performance compared with baselines without transformation, for the case where high quality graphs are available.

for recall measure, Our-Heter (N) produces the best result with the increasing number of top recommended items.


In this framework, two ways of consensus modeling are exploited.

the proposed models Our-Homo and Our-Heter can jointly provide a general and systematic solution to handling three real cases of using multiple graphs with item content information for recommendation: case 1) Heterogeneous networks with noise; case 2) Homogeneous networks; case 3) Heterogeneous networks with high quality.


Instead, we aim to combine generic features which are applicable to a variety of resources, and we show that combining them leads to state-of-the-art WSA performance.

we employ distances calculated with Dijkstra-WSA , an algorithm which works on graph representations of resources, as well as gloss similarity values.


Wiktionary-Wikipedia (German) On this dataset, the naive baselines are very strong, due to the disproportionately large number of positive examples -this is especially true for the 1:1 setup which reaches perfect precision.

whenever there is only one alignment candidate, it is already the correct one.


(5) Special right (4 labels): QUC, RAD, PUN, IS.

pUN and IS are common categories when appearing on the left side but special categories on the right side of the head.


The three ZPar-based dependency parsers gave higher accuracies than the two state-of-the-art constituent parsers.

the DS2PS outputs of Ours-PS parser outperforms the PS outputs of Berkeley Parer with 0.62% higher BF.


Figure 3(a) shows the correct PS while Figure 3(b) shows an incorrect parser output.

"i² (dawn)" is put under the incorrect constituent.


In this subtask, an item is the judgment of a clause pair.

if a document contains more than one clause pair that must be judged, the judgments for this document are divided into multiple items, although this is rare.


A2: 她鼓励我们兄弟姊妹学音乐和外语。 (She encouraged us to study music and foreign languages.)

in some cases, A1 and/or A2 may be equal to S. the annotators may think S is correct.


In summary, the upper bound of the correction performance would be 91.3%.

6%+2.7% of sentences cannot be resolved.


WePS is a competitive evaluation campaign that proposes several tasks including resolution of disambiguation on the Web data.

wePS-1, wePS-2 and wePS-3 campaigns provide an evaluation framework consisting in several annotated data sets composed of English person names.


We address the problem of predicting the player's strength from the text of these annotations.

we want to predict the rank of the player at the point when a given game was played.


Common texts for both groups:  It has been observed that the group 2 user ratings have higher correlation with the sentence level features than the discourse level features.

features such as number of $(noun phrase), $(adjective), $(unique entity) and $(clauses) have high correlation with the text difficulty ratings provided by the minor"s group.


At the time of writing of this article, the JeuxDeMots consists in more than 6 800 000 relations betweeen 310 000 terms.

it contains over 150 000 hypernym is-a relations, 170 000 syn relations and 27 000 hyponym relations.


Relation transfer with refinements can conclusively be considered as a usefull and efficient tool for relation inference, and it may be really crucial as support for building information to be used in word sense disambiguation.

it can help proposing hypernyms for the target term when they are missing, making possible further deductions or inductions.


The application of supervised learning technologies to textual data from the humanities in order to shed light on stylometric questions has become more popular of late.

these approaches have been applied to questions from the field of translation studies, which concern the notion of translationese 1 detection in Italian and other languages, (Baroni and Bernardini, 2006;Ilisei et al., 2010;Ilisei and Inkpen, 2011;Popescu, 2011;Koppel and Ordan, 2011;Lembersky et al., 2011).


Generally, in the technical domain, which is suitable for TM application, the translations (especially for technical terms) are much more consistent than that in the news domain.

the same source phrase in various places tends to have exactly the same translation in technical domains.


"SMT" and "Model-III" denote that we do not merge the TM phrasepairs into the SMT phrase table during decoding.

they only use the original SMT phrase   "SMT + " and "Model-III + " mean that we merge the TM phrase-pairs into the SMT phrase table dynamically.


In this paper we investigate the use of techniques that can exploit the training instances from different domains to learn a QE model for a specific target domain for which there is a small amount of labeled data.

we are interested in approaches that allow not only learning from one single source domain but also from multiple source domains simultaneously, by leveraging the labels from all available data to improve results in a target domain.


We also improve the state-of-the-art of analogical learning by revisiting the aggregation step of the process.

we observe that ranking analogical candidates according to random forests improves the performance of the analogical device, over training a classifier, as proposed for instance in (Langlais, 2013).


Therefore, we devised engines which translate sequences of symbols (characters), without taking into account the notion of word.

1 spaces in forms were considered as any ordinary symbol.


Our analysis reveals that divergent communication is a common behaviour for a large proportion of each online community.

many community members receive a negative Zelig Quotient.


This seems to show that these results (rhetorical relations linked to central unit) are not so trivial, since the degree of agreement expected at higher level tree structures is lower.

the agreement at lower levels is higher than in the high level.


Futhermore, it could also be argued that the use of different verbs has to do not only with the field but also with the medium: the GMB corpus derives from texts published in a periodical while the TERM and ZTF corpora include texts published in Conference proceedings.

it could be argued that the medium influences the writing style, and consequently, impacts the verb classes used in the texts.


A manual study by Brown (1983) suggests that the average referential distance for animate and inanimate entities differs widely within the same document.

2 why it makes sense to distinguish between these two types.


We computed the underlying lexical similarity matrices using the same procedure as described in (Malioutov and Barzilay, 2006;Kazantseva and Szpakowicz, 2011), but using our own code.

we built a matrix of cosine similarities after removing stop words and weighting the underlying vectors by tf.idf values.


In this paper, we investigate the problem of searching for most similar tweets given a query tweet.

we propose a binary coding approach to render computationally efficient tweet comparisons that should benefit practical NLP applications, especially in the face of massive data scenarios.


Under the weighted matrix factorization framework, we extend each tweet by its t nearest neighbors.

for each tweet, we incorporate additional words from its neighboring tweets.


Overall, our results show that unmarked contexts either improve accuracy or do not affect classification results.

the improvements in accuracy are particularly significant for those classes for which there were difficulties to find enough occurrences in marked contexts in previous experiments, i.e.


Based on these assumptions, we propose a Joint Prediction Factor Graph (JPFG) model, which collectively predicts personal skills with help of both personal and skill connections.

the JPFG model provides a general framework to integrate three kinds of knowledge, i.e.


We extract skill connections from same person.

if two vectors are from the same person with different skills, we consider these two vectors share skill connections (e.g.


Since directly maximizing the conditional probability P θ (Y |X, G) is often intractable, we factorize the "global" probability as a product of "local" factor functions, each of which depends on a subset of the variables in the graph (Tang et al., 2013).

we use three kinds of functions to represent the local textual information of the vector (local textual attribute function), personal connection information between vectors (personal connection factor) and skill connection information between skills (skill connection factor), respectively.


Personal connection factor function g(y i , y j ) : For the personal correlation factor function, we define it through the pairwise network structure.

if a person i and another person j have a personal relationship, we define a personal connection factor function as follows: (3) The personal connections are defined Section 4, i.e., co major, co univ, co title, and co corp. We define that if two persons have at least one personal connection edge, they have a personal relationship.


Skill connection factor function h(y i , y j ): For the skill connection factor function, we define it through the pairwise network structure.

if vector i and vector j are from the same person with different skills, we define their skill connection influence factor function as follows: Where γ ij is the function weight, representing the influence degree of i on j.